This is an example of using interval data, filters, and formatting to make an assessment regarding how many tons of cooling would be required for an elevator machine room. In this particular case, the elevator machines served hydraulic elevators. Hydraulic elevators are basically a big piston that raises the elevator car and its load of passengers and freight to the desired level by pumping hydraulic fluid into the cylinder. To go down, the fluid is released from the cylinder back to a reservoir in a controlled manner for re-use in the next cycle.

Thinking Through How the Energy Comes and Goes

If you think through the energy flow associated with the hydraulic elevator process, it is probably something like this:

You start with electricity and use it to run a motor on a hydraulic pump.

The motor losses show up as heat in the room where the motor is located.

The motor shaft power shows up as work done on the hydraulic fluid, which also ultimately shows up as heat in the fluid.

The energy imparted to the hydraulic fluid by the pump is used to create pressure, which lifts the elevator car and its contents upward.

The elevated mass of the car and contents represent potential energy that is recovered with the fluid is released from the hydraulic cylinder through a valve between it and the reservoir, lowering the mass.

I got to thinking about this because we were working in a building where cooling the elevator machines was a an issue. Compounding the problem was that when the Owner would approach an engineer about helping them to size equipment to serve the load, they would never hear back from them, probably because the project was so small that it would be difficult to justify a revenue stream that would pay the consulting fee. In other words, the process of showing up on site, assessing the situation, doing some math, developing construction documents, etc. could easily cost as much as the actual equipment would cost, maybe even more given that we were not talking about a lot of tons.

The Owner kind of “got that” and had tried to work directly with a contractor to put the equipment in. But the contractor was uncertain how to size the machinery to match the load and as a result, kept “back burnering” the conversation. So they asked if we could help them find a solution.

I had never thought about this much before, but based on the energy flow process I outlined above, I concluded that the cooling load in the elevator machine room was going to come pretty close to being equal to the energy in the electricity that went into the process, which eventually would show up as heat in the machine room since that is where most of the components of the system resided, including the hydraulic fluid.

At a given instant, that probably is not exactly true since, for instance, if you lift a big load up to the top floor and take it off the elevator and leave it there, some of the energy you used would remain on the top floor as potential energy in the form of elevated mass. It should show back up again in the elevator machine room if that mass was moved back into the elevator and lowered back to the first floor. The lowering of the mass would be accomplished by bleeding the high pressure fluid in the elevator piston to the low pressure reservoir, eventually showing up as heat.

The question then became one of understanding the power consumption associated with the elevators and what that pattern looked like; was it the same every day and the same every hour of the day or did it vary? In addition, because of thermal mass and a number of other factors, it was highly unlikely that the peak cooling load was anywhere near the rated kW of the equipment.

Asking the Building about Elevator Cooling Requirements

To try to understand all of this, we logged power consumption of the elevators using a one minute sampling rate, which created an interval data file that I was able to manipulate. The building was home to a ballet company and included their performance venue, so there might be activity any time of the day and any day of the week.

There was a large freight elevator and a smaller passenger elevator. Since the equipment serving them was in the same machine room, I added the power consumption together to get an over-all picture of the energy input to the space for my load assessment. Here is what the time series data looked like for a typical week, a typical day, and the time around the noon hour, when things seemed the busiest.

Obviously, pretty spikey, and while the demand could be as high as 30 kW (8.5 tons) certainly the cooling load would not be that high since the duration of the power draw was so short.

Since I was interested in the cooling load, I converted the kW data tons, which reflected the instantaneous load for the sample interval. Then I plotted instantaneous tons versus hour of the day and day of the week, which gave me charts that looked like this once I applied my shading trick to the data points.

Clearly you could see the instantaneous 8 ton or so load associated with the elevator machine demanding 30 kw on any day of the week and at any time of the day. But I was sure that did not represent anything near a requirement for 8 tons of cooling due to the short duration of the event and the flywheel created by the thermal mass of the building and equipment.

Averaging Out the Instantaneous the Spikes

To try to get a sense of what the cooling load might actually be, I created an averaging window calculation that averaged the load for a 15 minute, 30 minute, 45 minute, and 60 minute sliding window. This would tend to “play down” the short duration random events and, I hoped, give a result that was a better reflection of what the actual cooling load looked like.

These screen shots should give you a sense of what I mean by a floating average.

Having created my data table, I plotted the average tons vs. time of day, which gave me this chart for the 60 minute average.

It was starting to look to me like the right size for a cooling system was going to be something in the 3-4 ton range that could also turn down with out short cycling and match a 0.5 to 1 ton load.

A Scatter Plot Trick

To understand what the impact of a shorter averaging window might be, I created this chart, which helped me feel pretty comfortable with the 3-4 ton capacity recommendation.

This looks like some sort of bar graph, but it is actually a scatter plot. The trick is in the formatting of the markers and in the selection of the time stamp. When I picked “Day of Week” as the x-axis variable, that meant that all of the data would line up in a neat little row that corresponded to the number of the day of the week associated with it. But with out some formatting all of the little markers would be sitting on top of each other, making it impossible to contrast the different data series and assess if there were a lot of events or very few events at any given tonnage.

By making the marker size for each of the series smaller than the previous (in Excel, the first series seems to be plotted behind the second series, etc. if you think of things as being on layers), the neat little rows turned into stacked bars of varying widths. The transparency shading trick I mentioned in the previous posts in this series made the bars fade as the frequency of occurrence dropped off.

The Bottom Line

So the bottom line on this example is that by having an understanding of a basic physical principle (conservation of energy) along with some familiarity with scatter plots and interval data, I was able to ask the building what it thought the correct tonnage would be for a cooling system for the elevator machines. The result gave the Owner a basis for working with a contractor to install the required equipment that was founded in the realities of the load it served.